Biologic therapies and the need for hip and knee replacement amongst patients with rheumatoid arthritis: A population-based epidemiology study using electronic medical records and registry data
Bibliographic record
Abstract
Registry reports indicate over 200,000 hip or knee replacements are performed annually in England, Wales and Northern Ireland, with approximately 1-2% carried out for inflammatory conditions such as rheumatoid arthritis (RA). The aim of this DPhil was to estimate the impact of biologic therapies on the need for major joint replacement amongst RA patients using observational health data. An interrupted time-series analysis was used to estimate the impact of NICE approval of biologics on population-level temporal trends of total hip replacement (THR) and total knee replacement (TKR) amongst RA patients in England and Wales. Similar analyses were repeated for Denmark and Ontario. Overall, these studies indicated a decrease in TKR but not THR for RA patients following the introduction of biologics. When the rates in non-RA patients were taken into account (in Denmark and Ontario only), there was an inferred reduction in both THR and TKR for RA patients within the biologic era. There was a lack of guidance on sample size planning for such analyses, so a simulation study was also conducted to estimate power in various time-series scenarios. A patient-level analysis was then conducted using UK registry data, applying various novel methodologies to account for the inherent problem of confounding by indication. The results suggested no significant impact of biologics on rates of joint replacement, although in age-stratified analyses biologics was associated with a 40% reduction in THR rates amongst patients ≥60 years old. To conclude, a reduction is observed in population-level rates of THR and TKR amongst RA patients (compared to non-RA patients) following the introduction of biologics. Patient-level analyses confirmed a favourable impact on THR rates amongst older patients, but otherwise no significant associations. More patient-level analyses are required to confirm and/or further elucidate the impact of biologic therapies on the need for joint replacement in RA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".